Voice is a core component in human-computer interaction. Audio keyword spotting or wake-word detection are used in voice assistants to identify waking keywords (e.g., "Hey GoogleTM", "Hey SiriTM" or "AlexaTM"). The assistants must continuously listen for the keyword in utterances without being power or resource hungry [1]}. Table REF  lists some keyword spotting, speech enhancement, and wake-words detection models geared towards microcontrollers. The use of lightweight depthwise-separable convolution, attention condensers, and recurrent units have generated models that are in the order of \(10^0\)  kB. Some of the models (e.g., FastGRNN, ShallowRNN, and ULP RNN) can even run on AVR RISC microcontrollers with 2 kB SRAM, while others are suitable for deployment on Cortex M class microcontrollers. The models typically operate on log Mel-spectrograms, which are short-time Fourier transforms transferred to the Mel scale [2]} and available on CMSIS-DSP library for embedded implementation. Most models use the Google Speech Commands Dataset for training, which has 35 words with 105,829 utterances from 2,618 speakers [3]}.
